from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(other_library="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 14 predictions (58.33%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	KNeighborsClassifier_brute_force	predict	0.024	100000	1000	100	2.128877	0.280239	NaN	0.000376	0.002129	brute	-1	1	0.663	0.177379	0.003063	0.687	12.001858	12.003647
4	KNeighborsClassifier_brute_force	predict	0.015	100000	1000	100	2.823729	0.029471	NaN	0.000283	0.002824	brute	-1	5	0.757	0.181691	0.008840	0.742	15.541412	15.559797
7	KNeighborsClassifier_brute_force	predict	0.007	100000	1000	100	2.106872	0.090814	NaN	0.000380	0.002107	brute	1	100	0.882	0.214225	0.000397	0.875	9.834852	9.834869
8	KNeighborsClassifier_brute_force	predict	1.000	100000	1	100	0.019170	0.000171	NaN	0.000042	0.019170	brute	1	100	1.000	0.008875	0.000180	0.000	2.160161	2.160606
10	KNeighborsClassifier_brute_force	predict	0.007	100000	1000	100	2.871416	0.158655	NaN	0.000279	0.002871	brute	-1	100	0.882	0.213490	0.000831	0.875	13.449861	13.449963
11	KNeighborsClassifier_brute_force	predict	1.000	100000	1	100	0.024110	0.002122	NaN	0.000033	0.024110	brute	-1	100	1.000	0.009144	0.000937	0.000	2.636675	2.650490
13	KNeighborsClassifier_brute_force	predict	0.015	100000	1000	100	2.059207	0.014497	NaN	0.000388	0.002059	brute	1	5	0.757	0.178978	0.000666	0.742	11.505365	11.505445
16	KNeighborsClassifier_brute_force	predict	0.024	100000	1000	100	1.199453	0.005205	NaN	0.000667	0.001199	brute	1	1	0.663	0.176742	0.002242	0.687	6.786464	6.787010
19	KNeighborsClassifier_brute_force	predict	0.071	100000	1000	2	1.807085	0.033507	NaN	0.000009	0.001807	brute	-1	1	0.896	0.026398	0.000226	0.967	68.455327	68.457837
22	KNeighborsClassifier_brute_force	predict	0.052	100000	1000	2	2.846872	0.101212	NaN	0.000006	0.002847	brute	-1	5	0.922	0.027683	0.000915	0.974	102.836865	102.892971
25	KNeighborsClassifier_brute_force	predict	0.046	100000	1000	2	1.964610	0.011847	NaN	0.000008	0.001965	brute	1	100	0.929	0.061925	0.001297	0.975	31.725776	31.732731
28	KNeighborsClassifier_brute_force	predict	0.046	100000	1000	2	2.757163	0.043060	NaN	0.000006	0.002757	brute	-1	100	0.929	0.061730	0.000367	0.975	44.664636	44.665423
31	KNeighborsClassifier_brute_force	predict	0.052	100000	1000	2	1.940457	0.004650	NaN	0.000008	0.001940	brute	1	5	0.922	0.027534	0.000116	0.974	70.475325	70.475950
34	KNeighborsClassifier_brute_force	predict	0.071	100000	1000	2	1.093611	0.007729	NaN	0.000015	0.001094	brute	1	1	0.896	0.026411	0.000181	0.967	41.406813	41.407790

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.151	0.0	-1	1	0.049	0.004	0.231	0.231	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.080	0.0	-1	5	0.048	0.000	0.237	0.237	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.096	0.0	1	100	0.047	0.000	0.242	0.242	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.235	0.0	-1	100	0.048	0.000	0.231	0.231	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.329	0.0	1	5	0.047	0.000	0.230	0.230	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.246	0.0	1	1	0.048	0.000	0.231	0.231	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.367	0.0	-1	1	0.009	0.000	0.499	0.499	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.378	0.0	-1	5	0.009	0.000	0.485	0.485	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.364	0.0	1	100	0.008	0.000	0.519	0.519	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.374	0.0	-1	100	0.009	0.000	0.496	0.497	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.378	0.0	1	5	0.008	0.000	0.500	0.500	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.374	0.0	1	1	0.009	0.000	0.503	0.503	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.129	0.280	0.000	0.002	-1	1	0.177	0.003	12.002	12.004	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.002	0.000	0.024	-1	1	0.008	0.000	2.813	2.814	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.824	0.029	0.000	0.003	-1	5	0.182	0.009	15.541	15.560	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.003	0.000	0.023	-1	5	0.008	0.000	2.787	2.788	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.107	0.091	0.000	0.002	1	100	0.214	0.000	9.835	9.835	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.000	0.000	0.019	1	100	0.009	0.000	2.160	2.161	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.871	0.159	0.000	0.003	-1	100	0.213	0.001	13.450	13.450	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.002	0.000	0.024	-1	100	0.009	0.001	2.637	2.650	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.059	0.014	0.000	0.002	1	5	0.179	0.001	11.505	11.505	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.020	0.001	0.000	0.020	1	5	0.009	0.000	2.286	2.286	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.199	0.005	0.001	0.001	1	1	0.177	0.002	6.786	6.787	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.000	0.000	0.019	1	1	0.009	0.000	2.077	2.077	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.807	0.034	0.000	0.002	-1	1	0.026	0.000	68.455	68.458	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.004	0.000	0.006	-1	1	0.001	0.000	8.890	8.976	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.847	0.101	0.000	0.003	-1	5	0.028	0.001	102.837	102.893	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.002	0.000	0.007	-1	5	0.001	0.000	9.744	9.822	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.965	0.012	0.000	0.002	1	100	0.062	0.001	31.726	31.733	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	4.029	4.076	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.757	0.043	0.000	0.003	-1	100	0.062	0.000	44.665	44.665	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.003	0.000	0.006	-1	100	0.001	0.000	8.042	8.148	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.940	0.005	0.000	0.002	1	5	0.028	0.000	70.475	70.476	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	4.439	4.490	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.094	0.008	0.000	0.001	1	1	0.026	0.000	41.407	41.408	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.680	2.697	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 12 predictions (50.0%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	KNeighborsClassifier_kd_tree	predict	0.019	1000000	1000	10	0.822431	0.945738	NaN	0.000097	0.000822	kd_tree	-1	1	0.929	0.118045	0.001756	0.910	6.967104	6.967875
4	KNeighborsClassifier_kd_tree	predict	0.005	1000000	1000	10	0.982356	0.301064	NaN	0.000081	0.000982	kd_tree	-1	5	0.946	0.207160	0.001510	0.941	4.742026	4.742152
7	KNeighborsClassifier_kd_tree	predict	0.011	1000000	1000	10	5.425400	0.346854	NaN	0.000015	0.005425	kd_tree	1	100	0.951	0.628470	0.007008	0.940	8.632716	8.633253
10	KNeighborsClassifier_kd_tree	predict	0.011	1000000	1000	10	3.143240	0.243197	NaN	0.000025	0.003143	kd_tree	-1	100	0.951	0.608801	0.016553	0.940	5.163001	5.164910
13	KNeighborsClassifier_kd_tree	predict	0.005	1000000	1000	10	1.580989	0.122073	NaN	0.000051	0.001581	kd_tree	1	5	0.946	0.208965	0.002489	0.941	7.565825	7.566362
16	KNeighborsClassifier_kd_tree	predict	0.019	1000000	1000	10	0.873094	0.101119	NaN	0.000092	0.000873	kd_tree	1	1	0.929	0.112327	0.000928	0.910	7.772759	7.773024
19	KNeighborsClassifier_kd_tree	predict	0.012	1000	1000	2	0.026312	0.012533	NaN	0.000608	0.000026	kd_tree	-1	1	0.891	0.000397	0.000043	0.879	66.273894	66.662519
22	KNeighborsClassifier_kd_tree	predict	0.006	1000	1000	2	0.022179	0.000887	NaN	0.000721	0.000022	kd_tree	-1	5	0.911	0.000640	0.000031	0.905	34.666595	34.707293
25	KNeighborsClassifier_kd_tree	predict	0.023	1000	1000	2	0.034549	0.005975	NaN	0.000463	0.000035	kd_tree	1	100	0.894	0.004486	0.000021	0.917	7.700813	7.700901
28	KNeighborsClassifier_kd_tree	predict	0.023	1000	1000	2	0.036180	0.003305	NaN	0.000442	0.000036	kd_tree	-1	100	0.894	0.005276	0.001011	0.917	6.857128	6.981983
31	KNeighborsClassifier_kd_tree	predict	0.006	1000	1000	2	0.020111	0.000106	NaN	0.000796	0.000020	kd_tree	1	5	0.911	0.000642	0.000022	0.905	31.341430	31.360172
34	KNeighborsClassifier_kd_tree	predict	0.012	1000	1000	2	0.018866	0.000149	NaN	0.000848	0.000019	kd_tree	1	1	0.891	0.000381	0.000027	0.879	49.485903	49.610924

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.332	0.068	0.024	0.0	-1	1	0.776	0.088	4.294	4.322	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.659	0.054	0.022	0.0	-1	5	0.734	0.012	4.988	4.988	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.496	0.084	0.023	0.0	1	100	0.729	0.006	4.792	4.792	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.618	0.073	0.022	0.0	-1	100	0.727	0.009	4.976	4.977	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.372	0.061	0.024	0.0	1	5	0.726	0.005	4.643	4.643	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.487	0.058	0.023	0.0	1	1	0.731	0.008	4.770	4.771	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.021	0.0	-1	1	0.003	0.001	0.287	0.321	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.028	0.0	-1	5	0.001	0.001	0.489	0.638	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	100	0.001	0.001	0.512	0.588	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	-1	100	0.001	0.000	0.630	0.635	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	5	0.001	0.000	0.640	0.642	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	1	0.001	0.000	0.672	0.672	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.822	0.946	0.000	0.001	-1	1	0.118	0.002	6.967	6.968	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.002	0.000	0.003	-1	1	0.000	0.000	12.339	13.178	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.982	0.301	0.000	0.001	-1	5	0.207	0.002	4.742	4.742	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.000	0.000	7.947	8.428	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.425	0.347	0.000	0.005	1	100	0.628	0.007	8.633	8.633	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	1	100	0.001	0.000	4.187	4.432	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.143	0.243	0.000	0.003	-1	100	0.609	0.017	5.163	5.165	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	0.000	0.005	-1	100	0.001	0.000	6.522	6.888	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.581	0.122	0.000	0.002	1	5	0.209	0.002	7.566	7.566	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.001	0.000	0.002	1	5	0.000	0.000	3.708	3.988	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.873	0.101	0.000	0.001	1	1	0.112	0.001	7.773	7.773	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	3.913	4.193	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.026	0.013	0.001	0.000	-1	1	0.000	0.000	66.274	66.663	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.001	0.000	0.002	-1	1	0.000	0.000	27.669	28.931	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.022	0.001	0.001	0.000	-1	5	0.001	0.000	34.667	34.707	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	22.378	23.158	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.035	0.006	0.000	0.000	1	100	0.004	0.000	7.701	7.701	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	5.491	5.648	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.036	0.003	0.000	0.000	-1	100	0.005	0.001	6.857	6.982	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	100	0.000	0.000	21.617	22.365	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.020	0.000	0.001	0.000	1	5	0.001	0.000	31.341	31.360	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	6.394	6.614	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.019	0.000	0.001	0.000	1	1	0.000	0.000	49.486	49.611	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	6.764	7.022	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.507	0.059	30	0.032	0.0	random	0.377	0.022	1.344	1.347	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.556	0.011	30	0.029	0.0	k-means++	0.406	0.025	1.368	1.370	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.512	0.158	30	0.145	0.0	random	2.660	0.069	2.072	2.073	See	See
9	KMeans_tall	fit	1000000	1000000	100	5.714	0.041	30	0.140	0.0	k-means++	2.805	0.022	2.037	2.037	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.001	0.000	30	0.011	0.000	random	0.0	0.0	8.256	12.761	See	See
2	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	random	0.0	0.0	9.416	14.262	See	See
4	KMeans_tall	predict	1000000	1000	2	0.001	0.000	30	0.011	0.000	k-means++	0.0	0.0	10.594	12.247	See	See
5	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	k-means++	0.0	0.0	12.974	13.691	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.001	30	0.438	0.000	random	0.0	0.0	7.317	7.984	See	See
8	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	random	0.0	0.0	11.723	12.025	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.482	0.000	k-means++	0.0	0.0	6.590	7.124	See	See
11	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	k-means++	0.0	0.0	13.242	13.774	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 4 predictions (50.0%).
The observed differences can be found in the diff_adjusted_rand_scores column.
The chosen difference threshold is 0.001 for adjusted_rand_score.
See details in the dataframe below.

	estimator	function	diff_adjusted_rand_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup
1	KMeans_short	predict	0.001090	10000	1000	2	0.001783	0.000186	20	0.008972	0.000002	full	random	20	300	1	1.000000e-16	0.000126	0.000418	0.000025	-0.000965	4.262749	4.270442
4	KMeans_short	predict	0.001995	10000	1000	2	0.001755	0.000134	20	0.009115	0.000002	full	k-means++	20	300	1	1.000000e-16	0.001245	0.000443	0.000046	-0.000750	3.962905	3.983765
7	KMeans_short	predict	0.015034	10000	1000	100	0.002475	0.000265	20	0.323267	0.000002	full	random	20	300	1	1.000000e-16	0.278733	0.000928	0.000073	0.293767	2.667425	2.675672
10	KMeans_short	predict	0.060044	10000	1000	100	0.002381	0.000160	20	0.336029	0.000002	full	k-means++	20	300	1	1.000000e-16	0.317011	0.000938	0.000090	0.256968	2.537614	2.549158

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.072	0.000	20	0.002	0.0	random	0.026	0.002	2.772	2.780	See	See
3	KMeans_short	fit	10000	10000	2	0.207	0.001	20	0.001	0.0	k-means++	0.082	0.000	2.512	2.512	See	See
6	KMeans_short	fit	10000	10000	100	0.189	0.001	20	0.042	0.0	random	0.105	0.001	1.795	1.795	See	See
9	KMeans_short	fit	10000	10000	100	0.556	0.007	20	0.014	0.0	k-means++	0.304	0.000	1.825	1.825	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	random	0.000	0.0	4.263	4.270	See	See
2	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	0.000	0.0	13.349	13.660	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	k-means++	0.000	0.0	3.963	3.984	See	See
5	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	k-means++	0.000	0.0	12.980	13.433	See	See
7	KMeans_short	predict	10000	1000	100	0.002	0.0	20	0.323	0.000	random	0.001	0.0	2.667	2.676	See	See
8	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	random	0.000	0.0	10.706	10.912	See	See
10	KMeans_short	predict	10000	1000	100	0.002	0.0	20	0.336	0.000	k-means++	0.001	0.0	2.538	2.549	See	See
11	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	k-means++	0.000	0.0	10.382	10.601	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 2 predictions (50.0%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	LogisticRegression	predict	0.01	1000000	1000	100	0.000359	0.000357	[20]	2.226769	3.592650e-07	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.56	0.000601	0.000925	0.55	0.597755	1.097477
4	LogisticRegression	predict	0.07	1000	100	10000	0.001579	0.000373	[26]	5.067293	1.578752e-05	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.35	0.005017	0.000851	0.28	0.314701	0.319197

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	11.123	0.416	[20]	0.072	0.000	1.915	0.013	5.807	5.807	See	See
3	LogisticRegression	fit	1000	1000	10000	0.889	0.401	[26]	0.090	0.001	0.844	0.031	1.053	1.054	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	2.227	0.0	0.001	0.001	0.598	1.097	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.015	0.0	0.000	0.000	0.402	0.408	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	5.067	0.0	0.005	0.001	0.315	0.319	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	1.087	0.0	0.001	0.000	0.049	0.049	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 1 prediction (25.0%).
The observed differences can be found in the diff_r2_scores column.
The chosen difference threshold is 0.001 for r2_score.
See details in the dataframe below.

	estimator	function	diff_r2_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup
1	Ridge	predict	0.039624	1000	1000	10000	0.011297	0.000278	NaN	7.081458	0.000011	1.0	True	True	NaN	deprecated	NaN	auto	0.001	0.082567	0.018065	0.000127	0.122191	0.625343	0.625359

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.170	0.002	0.470	0.0	0.178	0.000	0.957	0.957	See	See
3	Ridge	fit	1000000	1000000	100	1.085	0.052	0.738	0.0	0.314	0.286	3.451	4.666	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.011	0.0	7.081	0.0	0.018	0.0	0.625	0.625	See	See
2	Ridge	predict	1000	1	10000	0.000	0.0	1.361	0.0	0.000	0.0	0.594	0.643	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.0	5.637	0.0	0.000	0.0	0.425	0.666	See	See
5	Ridge	predict	1000000	1	100	0.000	0.0	0.017	0.0	0.000	0.0	0.600	0.633	See	See